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Predict industrial equipment failure with time windows and transfer learning

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Abstract

Sensors, while more widely implemented in industry, have generated a large number of high-dimension unlabeled time series data during the process of the complicated producing. If putting these data to use, we can predict and preclude malfunctions of specific industrial facilities so that there will be less pecuniary lost. In this paper, we propose a malfunction predicting algorithm based on transfer learning. We use time windows due to the periodicity of industrial data, targeting at transfer learning among pieces of equipment with different sampling rate to address the problem of learning from unlabeled data. Rationale proofs and experiments indicate the efficacy of the algorithm and the prediction accuracy reaches 97%.

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References

  1. Bevilacqua M, Braglia M (2000) The analytic hierarchy process applied to maintenance strategy selection. Reliab Eng Syst Saf 70(1):71–83

    Article  Google Scholar 

  2. Carvalho TP, Soares FAAMN, Vita R, da Francisco R, P, Basto JP, Alcalá SGS (2019) A systematic literature review of machine learning methods applied to predictive maintenance. Comput Ind Eng 137:106024

  3. Csáji BC (2001) Approximation with artificial neural networks. Faculty of Sciences, Etvs Lornd University, Hungary, pp 24–48

    Google Scholar 

  4. Deng Z, Choi K-S, Jiang Y, Wang S (2014) Generalized hidden-mapping ridge regression, knowledge-leveraged inductive transfer learning for neural networks, fuzzy systems and kernel methods. IEEE Trans Cybern 44(12):2585–2599

    Article  Google Scholar 

  5. Deng Z, Jiang Y, Choi K-S, Chung F-L, Wang S (2013) Knowledge-leverage-based tsk fuzzy system modeling. IEEE Trans Neural Netw Learn Syst 24(8):1200–1212

    Article  Google Scholar 

  6. Deng Z, Jiang Y, Chung F-L, Ishibuchi H, Choi K-S, Wang S (2015) Transfer prototype-based fuzzy clustering. IEEE Trans Fuzzy Syst 24(5):1210–1232

    Article  Google Scholar 

  7. Deng Z, Jiang Y, Ishibuchi H, Choi K-S, Wang S (2016) Enhanced knowledge-leverage-based tsk fuzzy system modeling for inductive transfer learning. ACM Trans Intell Syst Technol (TIST) 8(1):11

    Google Scholar 

  8. Deng Z, Xu P, Xie L, Choi K-S, Wang S (2018) Transductive joint-knowledge-transfer tsk fs for recognition of epileptic eeg signals. IEEE Trans Neural Syst Rehabil Eng 26(8):1481–1494

    Article  Google Scholar 

  9. Do CB, Ng AY (2005) Transfer learning for text classification. Adv Neural Inf Proces Syst 299–306

  10. Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T (2014) Decaf: a deep convolutional activation feature for generic visual recognition. In International conference on machine learning, pages 647–655. PMLR

  11. Ferguson MK, Ronay AK, Lee Y-TT, Law KH (2018) Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning. Smart Sustain Manuf Syst, 2

  12. Gao J, Ling H, Hu W, Xing J (2014) Transfer learning based visual tracking with gaussian processes regression. In European Conference on Computer Vision, pages 188–203. Springer

  13. Han T, Liu C, Wu R, Jiang D (2021) Deep transfer learning with limited data for machinery fault diagnosis. Appl Soft Comput  103:107150

  14. Hoo-Chang S, Roth HR, Gao M, Le Lu ZX, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285

    Article  Google Scholar 

  15. Huang J-T, Li J, Yu D, Deng L, Gong Y (2013) Cross-language knowledge transfer using multilingual deep neural network with shared hidden layers. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 7304–7308. IEEE

  16. Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J Physiol 160(1):106–154

    Article  Google Scholar 

  17. Andrew KS, Jardine DL, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510

    Article  Google Scholar 

  18. Jiang Y, Wu D, Deng Z, Qian P, Wang J, Wang G, Chung F-L, Choi K-S, Wang S (2017) Seizure classification from eeg signals using transfer learning, semi-supervised learning and tsk fuzzy system. IEEE Trans Neural Syst Rehabil Eng 25(12):2270–2284

    Article  Google Scholar 

  19. Jung Y (2018) Multiple predicting k-fold cross-validation for model selection. J Nonparametric Stat 30(1):197–215

    Article  MathSciNet  Google Scholar 

  20. Kandaswamy C, Silva LM, Alexandre LA, Santos JM, de Sá JM (2014) Improving deep neural network performance by reusing features trained with transductive transference. In International Conference on Artificial Neural Networks, pages 265–272. Springer

  21. Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980

  22. Long M, Cao Y, Wang J, Jordan M (2015) Learning transferable features with deep adaptation networks. In International conference on machine learning, pages 97–105. PMLR

  23. Long M, Zhu H, Wang J, Jordan MI (2017) Deep transfer learning with joint adaptation networks. In International conference on machine learning, pages 2208–2217. PMLR

  24. Lu J, Behbood V, Hao P, Zuo H, Xue S, Zhang G (2015) Transfer learning using computational intelligence: a survey. Knowl-Based Syst 80:14–23

    Article  Google Scholar 

  25. Maron ME (1961) Automatic indexing: an experimental inquiry. J ACM (JACM) 8(3):404–417

    Article  Google Scholar 

  26. Pan SJ, Yang Q et al (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  27. Parisotto E, Ba JL, Salakhutdinov R (2015) Actor-mimic: Deep multitask and transfer reinforcement learning. arXiv preprint arXiv:1511.06342

  28. Pereira FLF, dos Santos Lima FD, de Moura Leite LG, Gomes JPP, de Castro Machado J (2017) Transfer learning for bayesian networks with application on hard disk drives failure prediction. In 2017 Brazilian Conference on Intelligent Systems (BRACIS), pages 228–233. IEEE

  29. Qian P, Zhao K, Jiang Y, Kuan-Hao S, Deng Z, Wang S, Muzic RF Jr (2017) Knowledge-leveraged transfer fuzzy c-means for texture image segmentation with self-adaptive cluster prototype matching. Knowl-Based Syst 130:33–50

    Article  Google Scholar 

  30. Vikas C, Raykar BK, Bi J, Dundar M, Rao RB (2008) Bayesian multiple instance learning: automatic feature selection and inductive transfer. In ICML 8:808–815

    Google Scholar 

  31. Roy DM, Kaelbling LP (2007) Efficient bayesian task-level transfer learning. IJCAI 7:2599–2604

    Google Scholar 

  32. Si J, Shi H, Chen J, Zheng C Unsupervised deep transfer learning with moment matching: A new intelligent fault diagnosis approach for bearings. Measurement 172:108827

  33. Silver DL, Mercer RE (2002) The task rehearsal method of life-long learning: Overcoming impoverished data. In Conference of the Canadian Society for Computational Studies of Intelligence, pages 90–101. Springer

  34. Chun S, Li L, Wen Z (2020) Remaining useful life prediction via a variational autoencoder and a time-window-based sequence neural network. Qual Reliab Eng Int 36(5):1639–1656

    Article  Google Scholar 

  35. Sun C, Ma M, Zhao Z, Tian S, Yan R, Chen X (2018) Deep transfer learning based on sparse autoencoder for remaining useful life prediction of tool in manufacturing. IEEE Trans Ind Inf 15(4):2416–2425

    Article  Google Scholar 

  36. Swietojanski P, Ghoshal A, Renals S (2012) Unsupervised cross-lingual knowledge transfer in dnn-based lvcsr. In Spoken Language Technology Workshop (SLT), pages 246–251. IEEE

  37. Taigman Y, Polyak A, Wolf L (2016) Unsupervised cross-domain image generation. arXiv preprint arXiv:1611.02200

  38. Xie L, Deng Z, Xu P, Choi K-S, Wang S (2018) Generalized hidden-mapping transductive transfer learning for recognition of epileptic electroencephalogram signals. IEEE Trans Cybern 49(6):2200–2214

    Article  Google Scholar 

  39. Yang C, Deng Z, Choi K-S, Wang S (2015) Takagi–sugeno–Kang transfer learning fuzzy logic system for the adaptive recognition of epileptic electroencephalogram signals. IEEE Trans Fuzzy Syst 24(5):1079–1094

    Article  Google Scholar 

  40. Yang H, Zhao F, Jiang G, Zheng S, Mei X (2019) A novel deep learning approach for machinery prognostics based on time windows. Appl Sci 9(22):4813

    Article  Google Scholar 

  41. Zhang W, Dong Y, Wang H (2019) Data-driven methods for predictive maintenance of industrial equipment: a survey. IEEE Syst J 13(3):2213–2227

    Article  Google Scholar 

  42. Zhu J, Chen N, Shen C (2019) A new deep transfer learning method for bearing fault diagnosis under different working conditions. IEEE Sensors J 20(15):8394–8402

    Article  Google Scholar 

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Acknowledgements

This paper was supported by NSFC grant U1866602.

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Authors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang, Wenbo Lu, Shihan Tang & Yang Song

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  1. Hongzhi Wang
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  2. Wenbo Lu
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  3. Shihan Tang
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  4. Yang Song
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Correspondence to Hongzhi Wang.

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Wang, H., Lu, W., Tang, S. et al. Predict industrial equipment failure with time windows and transfer learning. Appl Intell 52, 2346–2358 (2022). https://doi.org/10.1007/s10489-021-02441-z

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